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Neural Computing and Applications

, Volume 32, Issue 1, pp 235–245 | Cite as

Multi-source knowledge integration based on machine learning algorithms for domain ontology

  • Ting WangEmail author
  • Hanzhe Gu
  • Zhuang Wu
  • Jing Gao
Brain- Inspired computing and Machine learning for Brain Health

Abstract

In this paper, a new approach of automatic building for domain ontology based on machine learning algorithm is proposed, and by which the large-scale e-Gov ontology is built automatically. The advent of the knowledge graph era puts forward higher requirements for semantic search and analysis. Since traditional manual ontology construction requires the participation of domain experts in large-scale ontology construction, which will take time and considerable resources, and the ontology scale is also limited. The approach proposed in this paper not only makes up for the shortage of thesaurus description of the semantic relation between terms, but also takes advantage of the massive online encyclopedia knowledge and typical similarity algorithm in machine learning to fill the domain ontology automatically, so that the advantages of the two different knowledge sources are fully utilized and the system as a whole is gained. Ultimately, this may provide the foundation and support for the construction of knowledge graph and the semantic-oriented applications.

Keywords

Domain ontology Thesaurus Online encyclopedia Similarity computing 

Notes

Acknowledgements

This work was supported by the Scientific Research Project of Beijing Municipal Education Commission (General Social Science Project) and the Youth Excellent Teachers Grant of Capital University of Economics and Business (No. 23491854840429).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Information SchoolCapital University of Economics and BusinessBeijingChina
  2. 2.CTSC Center, Information SchoolCapital University of Economics and BusinessBeijingChina

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